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Why Three-Phase Seizure Segmentation Matters in SEEG
Published:
The central clinical question in drug-resistant focal epilepsy is not only where a seizure begins, but also how it evolves over time. In stereotactic EEG (SEEG), the transition from seizure onset to propagation and then to termination carries information that can help separate the true seizure onset zone from regions that are recruited later.
Why Changepoint Detection Fits SEEG Analysis
Published:
One reason I keep returning to changepoint methods in epilepsy research is that the underlying signals are fundamentally temporal. In stereotactic EEG (SEEG), clinically relevant events are not only defined by spatial location, but also by transitions in dynamics: onset, spread, reorganization, and termination.
Common Pitfalls in SEEG Machine Learning
Published:
Machine learning in EEG and SEEG can look deceptively straightforward: preprocess the signals, extract features or train a network, and report accuracy. In practice, many pipelines fail because the modeling choices are cleaner than the data assumptions underneath them.
portfolio
Deep Contrastive Learning for Seizure Onset Zone Localization
A self-supervised CNN-Transformer framework for automated ictal onset zone classification from SEEG spectrograms, with multi-center validation.
Seizure Propagation and Network Motif Reorganization in SEEG
Characterizing mesoscale directed network architecture during ictal evolution using three-node connectivity motifs in stereoelectroencephalography recordings.
Three-Phase Seizure Segmentation in SEEG
Semi-supervised changepoint detection for automated delineation of seizure onset, intra-ictal transition, and termination in stereoelectroencephalography recordings.
Spectral Graph Modeling of E/I Imbalance in Epilepsy
Biophysically-interpretable neural mass model parameters derived from resting-state MEG to identify excitatory-inhibitory imbalance in seizure onset zones.
publications
Correlation of the sEMG Signal and Muscle Stiffness during Fatiguing Isometric Contraction of Bicep Brachii Muscle in Dominant and Non-Dominant Hand
Published in International Conference on Mechanics of Functional Materials and Biological Tissues (ICMMB), 2017
Correlation of sEMG signal and muscle stiffness during fatiguing isometric contraction of bicep brachii in dominant and non-dominant hand.
Recommended citation: Navaneethkrishna, M., Nitinram, S., Kumar, H., Lasya, L., & Swaminathan, R. (2017). "Correlation of the sEMG Signal and Muscle Stiffness during Fatiguing Isometric Contraction of Bicep Brachii Muscle in Dominant and Non-Dominant Hand." ICMMB.
Feature Extraction to Detect Diabetic Retinopathy and Glaucoma in Fundus Image
Published in IRECS, 2018
Feature extraction approach to detect diabetic retinopathy and glaucoma from fundus images.
Recommended citation: Kumar, H., Agarwal, R., & Shatakshi, S. (2018). "Feature Extraction to Detect Diabetic Retinopathy and Glaucoma in Fundus Image." IRECS.
EMG and Grip Force Correlation at Varying Wrist Angles for Dominant vs. Non-Dominant Hand
Published in IEEE SCEECS, 2018
Correlation of EMG and grip force at varying wrist angles comparing dominant vs. non-dominant hand.
Recommended citation: Agarwal, R., Kumar, H., & Shatakshi, S. (2018). "EMG and Grip Force Correlation at Varying Wrist Angles for Dominant vs. Non-Dominant Hand." IEEE SCEECS.
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Differentiation of Valence Emotional States in Localized EEG Signals using Motif Patterns
Published in BIG BRAIN Conference, 2019
Differentiation of valence emotional states in localized EEG signals using motif patterns.
Recommended citation: Ganapathy, N., Kumar, H., & Swaminathan, R. (2019). "Differentiation of Valence Emotional States in Localized EEG Signals using Motif Patterns." BIG BRAIN Conference.
Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolutional Neural Network
Published in Journal of Medical Systems, 2021
Emotion recognition using electrodermal activity signals and multiscale deep convolutional neural network.
Recommended citation: Ganapathy, N., Veeranki, Y.R., Kumar, H., & Swaminathan, R. (2021). "Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolutional Neural Network." Journal of Medical Systems, 45, 49. DOI: 10.1007/s10916-020-01676-6
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Analysis of EEG Response for Audio-Visual Stimuli in Frontal Electrodes at Theta Frequency Band Using Topological Features
Published in Recent and Modern Biomedical Studies (RMBS), 2021
Analysis of EEG response for audio-visual stimuli in frontal electrodes at theta frequency band using topological features.
Recommended citation: Kumar, H., Puthankattil, S.D., & Swaminathan, R. (2021). "Analysis of EEG Response for Audio-Visual Stimuli in Frontal Electrodes at Theta Frequency Band Using Topological Features." Recent and Modern Biomedical Studies (RMBS).
EEG Based Emotion Recognition Using Entropy Features and Bayesian Optimized Random Forest
Published in Current Directions in Biomedical Engineering, 2021
EEG-based emotion recognition using entropy features and Bayesian optimized random forest classifier.
Recommended citation: Kumar, H., Ganapathy, N., Puthankattil, S.D., & Swaminathan, R. (2021). "EEG Based Emotion Recognition Using Entropy Features and Bayesian Optimized Random Forest." Current Directions in Biomedical Engineering, 7(2), 767–770. DOI: 10.1515/cdbme-2021-2196
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Emotion Recognition in EEG Signals Using Decision Fusion Based Electrode Selection
Published in Studies in Health Technology and Informatics, 2021
Emotion recognition in EEG signals using a decision fusion-based electrode selection strategy.
Recommended citation: Kumar, H., Ganapathy, N., Puthankattil, S.D., & Swaminathan, R. (2021). "Emotion Recognition in EEG Signals Using Decision Fusion Based Electrode Selection." Studies in Health Technology and Informatics, 281, 153–157. DOI: 10.3233/SHTI210139
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A Systematic Review of Sensing and Differentiating Dichotomous Emotional States Using Audio-Visual Stimuli
Published in IEEE Access, 2021
A systematic review of sensing and differentiating dichotomous emotional states using audio-visual stimuli.
Recommended citation: Veeranki, Y.R., Kumar, H., Ganapathy, N., Natarajan, B., & Swaminathan, R. (2021). "A Systematic Review of Sensing and Differentiating Dichotomous Emotional States Using Audio-Visual Stimuli." IEEE Access, 9, 124434–124451. DOI: 10.1109/ACCESS.2021.3110773
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Assessment of Emotional States in EEG Signals Using Phase Slope Index Based Functional Connectivity Features
Published in Recent and Modern Biomedical Studies (RMBS), 2022
Assessment of emotional states in EEG signals using phase slope index based functional connectivity features.
Recommended citation: Kumar, H., Ganapathy, N., Puthankattil, S.D., & Swaminathan, R. (2022). "Assessment of Emotional States in EEG Signals Using Phase Slope Index Based Functional Connectivity Features." Recent and Modern Biomedical Studies (RMBS).
Time and Frequency Domain Analysis of APB Muscles Abduction in Adult Dominant Hand Using Surface Electromyography Signals
Published in IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2022
Time and frequency domain analysis of APB muscle abduction in dominant hand using surface electromyography signals.
Recommended citation: Kumar, H., & Swaminathan, R. (2022). "Time and Frequency Domain Analysis of APB Muscles Abduction in Adult Dominant Hand Using Surface Electromyography Signals." IEEE MeMeA 2022. DOI: 10.1109/MeMeA54994.2022.9856551
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Classification of Emotional States Using EEG Signals and Wavelet Packet Transform Features
Published in Medical Informatics Europe (MIE) — Studies in Health Technology and Informatics, 2022
Classification of emotional states using EEG signals and wavelet packet transform features, presented at Medical Informatics Europe 2022.
Recommended citation: Kumar, H., Ganapathy, N., Puthankattil, S.D., & Swaminathan, R. (2022). "Classification of Emotional States Using EEG Signals and Wavelet Packet Transform Features." Studies in Health Technology and Informatics, 294, 943–944. DOI: 10.3233/SHTI220632
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Impulsivity Classification Using EEG-Based Features
Published in Organization for Human Brain Mapping (OHBM) Annual Meeting, 2022
Impulsivity classification using EEG-based features, presented at the Organization for Human Brain Mapping Annual Meeting 2022.
Recommended citation: Kumar, H., Hüpen, P., Swaminathan, R., & Habel, U. (2022). "Impulsivity Classification Using EEG-Based Features." Organization for Human Brain Mapping (OHBM) Annual Meeting.
Assessment of Emotional States in EEG Signals Using Multi-Frequency Power Spectrum and Functional Connectivity Patterns
Published in 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2022
Assessment of emotional states in EEG signals using multi-frequency power spectrum and functional connectivity patterns, presented at IEEE EMBC 2022, Glasgow.
Recommended citation: Kumar, H., Ganapathy, N., Puthankattil, S.D., & Swaminathan, R. (2022). "Assessment of Emotional States in EEG Signals Using Multi-Frequency Power Spectrum and Functional Connectivity Patterns." IEEE EMBC 2022, pp. 280–283. DOI: 10.1109/EMBC48229.2022.9871510
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Impulsivity Classification Using EEG Power and Explainable Machine Learning
Published in International Journal of Neural Systems, 2023
Impulsivity classification using EEG power features and explainable machine learning approaches. Joint first authorship.
Recommended citation: Hüpen*, P., Kumar*, H., Shymanskaya, A., Swaminathan, R., & Habel, U. (2023). "Impulsivity Classification Using EEG Power and Explainable Machine Learning." International Journal of Neural Systems, 33(2), 2350006. (*Joint first author) DOI: 10.1142/S0129065723500065
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Exploring Central-Peripheral Nervous System Interaction Through Multimodal Biosignals: A Systematic Review
Published in IEEE Access, 2024
A systematic review exploring central-peripheral nervous system interaction through multimodal biosignals.
Recommended citation: Banik, S., Kumar, H., Ganapathy, N., & Swaminathan, R. (2024). "Exploring Central-Peripheral Nervous System Interaction Through Multimodal Biosignals: A Systematic Review." IEEE Access, 12, 60347–60368. DOI: 10.1109/ACCESS.2024.3392069
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Assessment of Valence Emotional State Using EEG-EDA Coupling and Explainable Classifiers
Published in Medical Informatics Europe (MIE) — Studies in Health Technology and Informatics, 2024
Assessment of valence emotional state using EEG-EDA coupling and explainable classifiers.
Recommended citation: Banik, S., Kumar, H., Ganapathy, N., & Swaminathan, R. (2024). "Assessment of Valence Emotional State Using EEG-EDA Coupling and Explainable Classifiers." Studies in Health Technology and Informatics, 316, 953–957. DOI: 10.3233/SHTI240569
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Assessment of Emotion Elicitation using Multimodal Physiological Sensors and Phase Synchronization
Published in IEEE Sensors Letters, 2024
Assessment of emotion elicitation using multimodal physiological sensors and phase synchronization.
Recommended citation: Banik, S., Kumar, H., Ganapathy, N., & Swaminathan, R. (2024). "Assessment of Emotion Elicitation using Multimodal Physiological Sensors and Phase Synchronization." IEEE Sensors Letters. DOI: 10.1109/LSENS.2024.3426562
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Analysis of EEG Fluctuation Patterns using Nonlinear Phase-Based Functional Connectivity Measures for Emotion Recognition
Published in Fluctuation and Noise Letters, 2024
Analysis of EEG fluctuation patterns using nonlinear phase-based functional connectivity measures for emotion recognition.
Recommended citation: Kumar, H., Ganapathy, N., Puthankattil, S.D., & Swaminathan, R. (2024). "Analysis of EEG Fluctuation Patterns using Nonlinear Phase-Based Functional Connectivity Measures for Emotion Recognition." Fluctuation and Noise Letters, 23(5). DOI: 10.1142/S0219477524500512
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Assessment of EEG-PPG Cross Frequency Coherence under Evoked Emotional Arousal
Published in Current Directions in Biomedical Engineering, 2024
Assessment of EEG-PPG cross frequency coherence under evoked emotional arousal states.
Recommended citation: Banik, S., Kumar, H., Ganapathy, N., & Swaminathan, R. (2024). "Assessment of EEG-PPG Cross Frequency Coherence under Evoked Emotional Arousal." Current Directions in Biomedical Engineering, 10(4), 49–52. DOI: 10.1515/cdbme-2024-2012
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Functional Brain Networks of Trait Impulsivity: A Connectome-Based Predictive Modeling Analysis
Published in Human Brain Mapping, 2024
Functional brain networks of trait impulsivity investigated using connectome-based predictive modeling analysis.
Recommended citation: Hüpen, P., Kumar, H., Swaminathan, R., Habel, U., & Wagels, L. (2024). "Functional Brain Networks of Trait Impulsivity: A Connectome-Based Predictive Modeling Analysis." Human Brain Mapping, 45, e70059. DOI: 10.1002/hbm.70059
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Analysis of Dynamics of EEG Signals in Emotional Valence Using Super-Resolution Superlet Transform
Published in IEEE Sensors Letters, 2025
EEG-based emotional state assessment using super-resolution superlet transform for time-frequency analysis of emotional valence dynamics.
Recommended citation: Kumar, H., Ganapathy, N., & Swaminathan, R. (2025). "Analysis of Dynamics of EEG Signals in Emotional Valence Using Super-Resolution Superlet Transform." IEEE Sensors Letters, 9, 1–4. DOI: 10.1109/LSENS.2025.3526907
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Unsupervised Learning of Ictal Patterns from Stereo EEG using Contrastive Latent Representation
Published in American Epilepsy Society Annual Meeting, 2025
Unsupervised learning of ictal onset patterns from SEEG using contrastive latent representations, presented at AES Annual Meeting 2025.
Recommended citation: Kumar, H., Seshadri, G., Martinez, D., Najm, I., Alexopoulos, A., Bulacio, J.C., Serletis, D., & Krishnan, B. (2025). "Unsupervised Learning of Ictal Patterns from Stereo EEG using Contrastive Latent Representation." American Epilepsy Society Annual Meeting.
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Optimal Temporal Resolution for Detecting Functional Connectivity Microstates During Epileptic Seizures Using SEEG
Published in American Epilepsy Society Annual Meeting, 2025
Determining optimal temporal resolution for detecting functional connectivity microstates during epileptic seizures using SEEG.
Recommended citation: Banappa, H.S., Kumar, H., Seshadri, G., Alexopoulos, A., Martinez, D., Najm, I., Bulacio, J.C., Serletis, D., & Krishnan, B. (2025). "Optimal Temporal Resolution for Detecting Functional Connectivity Microstates During Epileptic Seizures Using SEEG." American Epilepsy Society Annual Meeting.
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Three-Phase Seizure Segmentation in Stereotactic EEG Using Envelope-Based Multivariate Changepoint Analysis
Published in Annals of Biomedical Engineering, 2026
Semi-supervised framework that automatically delineates three distinct seizure phases—ictal onset, intra-ictal transition, and seizure termination—from SEEG recordings using envelope-based features and PELT changepoint detection.
Recommended citation: Kumar, H., Seshadri, N.P.G., Martinez, D., Najm, I., Alexopoulos, A., Bulacio, J.C., Serletis, D., & Krishnan, B. (2026). "Three-Phase Seizure Segmentation in Stereotactic EEG Using Envelope-Based Multivariate Changepoint Analysis." Annals of Biomedical Engineering. DOI: 10.1007/s10439-026-04097-7
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EEG-Based Emotion Recognition using Super-Resolution Superlet Transform and Self-Attention Convolutional Neural Network
Published in Biomedical Signal Processing and Control, 2026
EEG-based emotion recognition combining super-resolution superlet transform with self-attention convolutional neural network for improved classification performance.
Recommended citation: Kumar, H., Govindarajan, S., Ramakrishnan, M.S., Karthick, P.A., & Ganapathy, N. (2026). "EEG-Based Emotion Recognition using Super-Resolution Superlet Transform and Self-Attention Convolutional Neural Network." Biomedical Signal Processing and Control.
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Subject-independent emotion recognition with EEG bispectral quadratic phase coupling features and explainable machine learning
Published in Biomedical Physics & Engineering Express, 2026
Subject-independent emotion recognition using EEG bispectral quadratic phase coupling features with explainable machine learning approaches.
Recommended citation: Kumar, H., Ganapathy, N., Puthankattil, S.D., & Swaminathan, R. (2026). "Subject-independent emotion recognition with EEG bispectral quadratic phase coupling features and explainable machine learning." Biomedical Physics & Engineering Express. DOI: 10.1088/2057-1976/ae711a
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